agricidaniel/claude-ads

ads-attribution

Audit cross-platform attribution, conversion definitions, reporting windows, GA4, AdServices and AdAttributionKit, MMPs, browser and server events, offline conversions, and platform reconciliation.

All-time #4924 Trending #4638 First seen May 18, 2026
8-week activity · all time api

Installation

$ npx skills add agricidaniel/claude-ads --skill ads-attribution

Summary

  • Audit cross-platform attribution, conversion definitions, reporting windows, GA4, AdServices and AdAttributionKit, MMPs, browser and server events, offline conversions, and platform reconciliation.
  • Use for attribution audit, attribution models, conversion windows, requests to add or total Meta and Google conversions, incompatible reporting-window aggregation, GA4 attribution, MMP review, AppsFlyer, Adjust, Branch, Singular, or cross-platform discrepancies.

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from agricidaniel/claude-ads · top by installs.

npx skills add agricidaniel/claude-ads

Browse all from agricidaniel/claude-ads

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 9.1K
License LICENSE
Default branch main
Open issues 19
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,995 B
  • docs SUMMARY.md 1,760 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2,700 installs

SKILL.md

Attribution Audit

  1. Read the main ads contract and normalized account snapshots.
  2. Declare the business conversion, value, data window, timezone, currency, and

decision the attribution analysis must support.

  1. Inventory every browser, server, platform, analytics, MMP, offline, and app

attribution source with its identity, counting, deduplication, and privacy rules.

  1. Reconcile comparable events and explain differences caused by eligibility,

view-through rules, consent, modeled data, conversion lag, thresholds, or scope.

  1. Separate measurement quality from platform-reported performance.
  2. Return findings, contradictions, confidence, missing evidence, and a measurement

improvement plan through the common JSON contract.

Do not assume one platform is ground truth, add incompatible reports together, or recommend an attribution model without the operator's decision context.

Comparability gate

Reject aggregation until the sources share, or are explicitly normalized to, the same conversion event and value definition, attribution window, click/view scope, counting method, deduplication identity, timezone, currency, attribution model, and modeled-data treatment. Until then, report the values side by side with their definitions; do not compute a total.

Example: Meta seven-day conversions and Google thirty-day conversions are incompatible. Refuse to add them, reconcile windows and definitions first, and only aggregate a newly comparable dataset.